Clustering and Artificial Neural Network Ensembles Based Effort Estimation
Bibliographic record
Abstract
Accurate effort estimation of software development projects plays a key role in project success.However, it is still a challenge activity to researchers and practitioners because of the nature of software products and dynamics in software industry and development environment.Artificial neural network (ANN) is as an effective method and has been widely used in various areas of software engineering.This paper proposes a new effort estimation method based on clustering and ANN ensembles.The contribution of the paper is twofold.First, the impact of clustering projects on the estimation accuracy is investigated.Second, the impact of using ANN ensembles instead of a single ANN is also investigated.The proposed method includes three phases called pre-processing, k-means clustering, and ANN ensembles effort estimation.The method starts with exploring the historical projects dataset.Afterward, k-means is used to cluster the projects.Finally, the proposed method as well as two other estimation methods (i.e. a single ANN and expert-based) were applied to the created clusters and results were compared using MMRE and PRED measures.The simulation results show that the proposed method significantly outperforms the two other estimation methods.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".